α-Synuclein and ALPS motifs are membrane curvature sensors whose contrasting chemistry mediates selective vesicle binding

Two membrane curvature–sensing molecules with opposite chemistries are targeted to distinct vesicle classes through direct interaction with different lipid environments

Learning a Prior on Regulatory Potential from eQTL Data

Genome-wide RNA expression data provide a detailed view of an organism's biological state; hence, a dataset measuring expression variation between genetically diverse individuals (eQTL data) may provide important insights into the genetics of complex traits. However, with data from a relatively small number of individuals, it is difficult to distinguish true causal polymorphisms from the large number of possibilities. The problem is particularly challenging in populations with significant linkage disequilibrium, where traits are often linked to large chromosomal regions containing many genes. Here, we present a novel method, Lirnet, that automatically learns a regulatory potential for each sequence polymorphism, estimating how likely it is to have a significant effect on gene expression. This regulatory potential is defined in terms of “regulatory features”—including the function of the gene and the conservation, type, and position of genetic polymorphisms—that are available for any organism. The extent to which the different features influence the regulatory potential is learned automatically, making Lirnet readily applicable to different datasets, organisms, and feature sets. We apply Lirnet both to the human HapMap eQTL dataset and to a yeast eQTL dataset and provide statistical and biological results demonstrating that Lirnet produces significantly better regulatory programs than other recent approaches. We demonstrate in the yeast data that Lirnet can correctly suggest a specific causal sequence variation within a large, linked chromosomal region. In one example, Lirnet uncovered a novel, experimentally validated connection between Puf3—a sequence-specific RNA binding protein—and P-bodies—cytoplasmic structures that regulate translation and RNA stability—as well as the particular causative polymorphism, a SNP in Mkt1, that induces the variation in the pathway

Conformational Basis for Asymmetric Seeding Barrier in Filaments of Three- and Four-Repeat Tau

*S Supporting Information ABSTRACT: Tau pathology in Alzheimer’s disease is intimately linked to the deposition of proteinacious filaments, which akin to infectious prions, have been proposed to spread via seeded conversion. Here we use double electron−electron resonance (DEER) spectroscopy in combination with extensive computational analysis to show that filaments of three- (3R) and four-repeat (4R) tau are conformationally distinct. Distance measurements between spin labels in the third repeat, reveal tau amyloid filaments as ensembles of known β-strand−turn−β-strand U-turn motifs. Whereas filaments seeded with 3R tau are structurally homogeneous, filaments seeded with 4R tau are heterogeneous, composed of at least three distinct conformers. These findings establish a molecular basis for the seeding barrier between different tau isoforms and offer a new powerful approach for investigating the composition and dynamics of amyloid fibril ensembles